Design for How People Think

[ Index ]

A

abstract concepts, 31–37

acuity, visual, 16–17

affinities and psychological profiles, 141–143

AI Winter, 196

Alexa (Amazon), 28–29, 198

Amazon Alexa, 28–29, 198

analyzing dreams, 55, 59–60, 131–133

analyzing user research. See Post-It note categorization method

analyzing word frequency, 96–97

appeal (form of emotion), 130–131, 161–163, 175

Apple Siri, 28, 198–199

Ariely, Dan, 57

artifacts (what researchers notice), 68

artificial intelligence

artificial neural networks, 197–199

background information, 196–197

recommendations for, 202

statistical learning, 197–199

Assistant app (Google), 198

assumptions

challenging internal, 155–156

contextual interviews and, 71, 81

empathy research and, 65–66

attention. See vision, attention, and automaticity

audience segmentation

case study, Millennial money, 152–154

case study, trust in credit, 154

challenging internal assumptions, 155–156

creating, 77–78, 113, 141, 152–154

emotion and, 147–148

empathy research and, 155–158

finding the dimensions, 152–155

identifying, 117

language and, 143–146

psychographic profiles, 131–132, 141–143

recommendations for, 160

wayfinding and, 149–151

augmented reality (AR), 24

automaticity. See vision, attention, and automaticity

awaken (form of emotion), 130–131, 165–166, 175

B

behaviors, unconscious, 12–14, 64, 70

blockers (problems), 52–53

boundary extension, 35–36

brain

artificial intelligence and, 196–198

spatial information and, 19–21

too much information, 56–57

what information/pathway, 9